diff options
| -rw-r--r-- | utils/raspberrypi/ctt/ctt_macbeth_locator.py | 69 |
1 files changed, 40 insertions, 29 deletions
diff --git a/utils/raspberrypi/ctt/ctt_macbeth_locator.py b/utils/raspberrypi/ctt/ctt_macbeth_locator.py index 3e95df89..178aeed0 100644 --- a/utils/raspberrypi/ctt/ctt_macbeth_locator.py +++ b/utils/raspberrypi/ctt/ctt_macbeth_locator.py @@ -57,6 +57,10 @@ def find_macbeth(Cam, img, mac_config=(0, 0)): """ cor, mac, coords, msg = get_macbeth_chart(img, ref_data) + # Keep a list that will include this and any brightened up versions of + # the image for reuse. + all_images = [img] + """ following bits of code tries to fix common problems with simple techniques. @@ -71,6 +75,7 @@ def find_macbeth(Cam, img, mac_config=(0, 0)): if cor < 0.75: a = 2 img_br = cv2.convertScaleAbs(img, alpha=a, beta=0) + all_images.append(img_br) cor_b, mac_b, coords_b, msg_b = get_macbeth_chart(img_br, ref_data) if cor_b > cor: cor, mac, coords, msg = cor_b, mac_b, coords_b, msg_b @@ -81,6 +86,7 @@ def find_macbeth(Cam, img, mac_config=(0, 0)): if cor < 0.75: a = 4 img_br = cv2.convertScaleAbs(img, alpha=a, beta=0) + all_images.append(img_br) cor_b, mac_b, coords_b, msg_b = get_macbeth_chart(img_br, ref_data) if cor_b > cor: cor, mac, coords, msg = cor_b, mac_b, coords_b, msg_b @@ -128,23 +134,26 @@ def find_macbeth(Cam, img, mac_config=(0, 0)): h_inc = int(h/6) """ for each subselection, look for a macbeth chart + loop over this and any brightened up images that we made to increase the + likelihood of success """ - for i in range(3): - for j in range(3): - w_s, h_s = i*w_inc, j*h_inc - img_sel = img[w_s:w_s+w_sel, h_s:h_s+h_sel] - cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data) - """ - if the correlation is better than the best then record the - scale and current subselection at which macbeth chart was - found. Also record the coordinates, macbeth chart and message. - """ - if cor_ij > cor: - cor = cor_ij - mac, coords, msg = mac_ij, coords_ij, msg_ij - ii, jj = i, j - w_best, h_best = w_inc, h_inc - d_best = 1 + for img_br in all_images: + for i in range(3): + for j in range(3): + w_s, h_s = i*w_inc, j*h_inc + img_sel = img_br[w_s:w_s+w_sel, h_s:h_s+h_sel] + cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data) + """ + if the correlation is better than the best then record the + scale and current subselection at which macbeth chart was + found. Also record the coordinates, macbeth chart and message. + """ + if cor_ij > cor: + cor = cor_ij + mac, coords, msg = mac_ij, coords_ij, msg_ij + ii, jj = i, j + w_best, h_best = w_inc, h_inc + d_best = 1 """ scale 2 @@ -157,17 +166,19 @@ def find_macbeth(Cam, img, mac_config=(0, 0)): h_sel = int(h/2) w_inc = int(w/8) h_inc = int(h/8) - for i in range(5): - for j in range(5): - w_s, h_s = i*w_inc, j*h_inc - img_sel = img[w_s:w_s+w_sel, h_s:h_s+h_sel] - cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data) - if cor_ij > cor: - cor = cor_ij - mac, coords, msg = mac_ij, coords_ij, msg_ij - ii, jj = i, j - w_best, h_best = w_inc, h_inc - d_best = 2 + # Again, loop over any brightened up images as well + for img_br in all_images: + for i in range(5): + for j in range(5): + w_s, h_s = i*w_inc, j*h_inc + img_sel = img_br[w_s:w_s+w_sel, h_s:h_s+h_sel] + cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data) + if cor_ij > cor: + cor = cor_ij + mac, coords, msg = mac_ij, coords_ij, msg_ij + ii, jj = i, j + w_best, h_best = w_inc, h_inc + d_best = 2 """ The following code checks for macbeth charts at even smaller scales. This @@ -238,7 +249,7 @@ def find_macbeth(Cam, img, mac_config=(0, 0)): print error or success message """ print(msg) - Cam.log += '\n' + msg + Cam.log += '\n' + str(msg) if msg == success_msg: coords_fit = coords Cam.log += '\nMacbeth chart vertices:\n' @@ -606,7 +617,7 @@ def get_macbeth_chart(img, ref_data): '\nNot enough squares found' '\nPossible problems:\n' '- Macbeth chart is occluded\n' - '- Macbeth chart is too dark of bright\n' + '- Macbeth chart is too dark or bright\n' ) ref_cents = np.array(ref_cents) |